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Title:Prediction of technological parameters of sheet metal bending in two stages using feed-forward neural network
Authors:ID Šenveter, Jernej (Author)
ID Balič, Jože (Author)
ID Ficko, Mirko (Author)
ID Klančnik, Simon (Author)
Files:.pdf Tehnicki_vjesnik_2016_Senveter_et_al._Prediction_of_technological_parameters_of_sheet_metal_bending_in_two_stages_using_feed_forward_neu.pdf (900,30 KB)
MD5: EB243D7F9CE550584AAC73D47E5A3F10
PID: 20.500.12556/dkum/6a0e0eb5-272b-42b4-9c75-59197b70c836
 
URL http://hrcak.srce.hr/163794
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract:This paper describes sheet metal bending in two stages as well as predicting and testing of the final bend angle by means of a feed-forward neural network. The primary objective was to research the technological parameters of bending sheet metal in two stages and to develop an intelligent method that would enable the predicting of those technological parameters. The process of bending sheet metal in two stages is presented by demonstrating the various technological parameters and the test tool used to carry out tests and measurements. The results of the tests and measurements were of decisive guidance in the evaluation of individual technological parameters. Developed method for prediction of the final bend angle is based on a feed-forward neural network that receives signals at the input level. These signals then travel through the hidden level to the output level, where the responses to input signals are received. The input to the neural network is composed of data that affect the selection of the final bend angle. Only five different inputs are used for the total neural network. By choosing the desired final bend angle by means of the trained neural network, bending sheet metal in two stages is optimised and made more efficient.
Keywords:bending in two stages, intelligent system, neural network, prediction of the final bend angle
Publication status:Published
Publication version:Version of Record
Year of publishing:2016
Number of pages:str. 1155-1161
Numbering:Letn. 23, št. 4
PID:20.500.12556/DKUM-66824 New window
ISSN:1330-3651
UDC:004.896:621.981
ISSN on article:1330-3651
COBISS.SI-ID:19735062 New window
DOI:10.17559/TV-20141201225004 New window
NUK URN:URN:SI:UM:DK:QEYWLGEH
Publication date in DKUM:12.07.2017
Views:1467
Downloads:504
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku
Shortened title:Teh. vjesn. - Stroj. fak.
Publisher:Strojarski fakultet, Elektrotehnički fakultet, Građevinski fakultet
ISSN:1330-3651
COBISS.SI-ID:15346181 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:12.07.2017

Secondary language

Language:Croatian
Title:Predviđanje tehnoloških parametara savijanja lima u dvije faze pomoću usmjerene neuronske mreže
Abstract:Članak prikazuje savijanje lima u dvije faze i predviđanje konačnog kuta savijanja pomoću usmjerene neuronske mreže. Glavni cilj je bio istražiti tehnološke parametre savijanja lima u dvije faze i razviti inteligentan način, koji će omogućiti predviđanje tih tehnoloških parametara. Prikazan je proces savijanja lima u dvije faze, gdje se prikazuju i razni tehnološki parametri i ispitni alati sa kojima su provedena ispitivanja i mjerenja. Rezultati ispitivanja i mjerenja su bili ključ u donošenju procjene pojedinih tehnoloških parametara. Opisano je predviđanje konačnog kuta savijanja lima korištenjem usmjerene neuronske mreže, koja prima signale na ulazu. Ti signali tada prolaze kroz skrivenu razinu do izlaza, gdje dobiju odgovor na ulazne signale. Za ulaz u neuronsku mrežu upotrebljavaju se podaci koji utječu na odabir kuta konačnog savijanja. Za neuronsku mrežu se koristi pet različitih inputa. Odabirom željenog kuta savijanja pomoću neuronske mreže, može se doprinijeti optimizaciji savijanja lima u dvije faze.
Keywords:upogibanje v dveh stopnjah, inteligentni sistemi, nevronske mreže, napovedovanje končnega kota upogiba


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